Graph neural networks (GNNs) were first introduced in [1]} and have become a key component for handling structured data [2]}. Given their expressive power [3]}, GNNs have been applied to a wide variety of domains [2]}. Several architectures have been proposed, ranging from simple aggregating methods such as Graph Convolutional Network (GCN) [5]} and Message Passing Neural Network (MPNN) [6]}, [7]} to advanced architectures like Graph Attention Network (GAT) [8]} and GraphSAGE [9]}. Given a graph \(\mathcal {G = (\mathcal {V}, \mathcal {E})}\)  with \(\mathcal {V} = \lbrace v_1, \dots , v_{n}\rbrace \)  vertices and \(\mathcal {E} = \lbrace  e_1, \dots , e_{m} \rbrace \)  edges, a GNN generally employs the following message-passing aggregation function:
\(H^{t+1} = f(A, H^t; \theta ^{t+1})\) 
